Package org.hipparchus.analysis.interpolation

Class AkimaSplineInterpolator

java.lang.Object

org.hipparchus.analysis.interpolation.AkimaSplineInterpolator

All Implemented Interfaces:

FieldUnivariateInterpolator, UnivariateInterpolator

public class AkimaSplineInterpolator
extends Object
implements UnivariateInterpolator, FieldUnivariateInterpolator

Computes a cubic spline interpolation for the data set using the Akima algorithm, as originally formulated by Hiroshi Akima in his 1970 paper "A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures." J. ACM 17, 4 (October 1970), 589-602. DOI=10.1145/321607.321609 http://doi.acm.org/10.1145/321607.321609

This implementation is based on the Akima implementation in the CubicSpline class in the Math.NET Numerics library. The method referenced is CubicSpline.InterpolateAkimaSorted

The interpolate method returns a PolynomialSplineFunction consisting of n cubic polynomials, defined over the subintervals determined by the x values, x[0] < x[i] ... < x[n]. The Akima algorithm requires that n >= 5.

Constructor Summary

Constructors

Constructor	Description
AkimaSplineInterpolator()	Simple constructor.
AkimaSplineInterpolator(boolean useModifiedWeights)	Simple constructor.

Method Summary

Modifier and Type	Method	Description
PolynomialSplineFunction	interpolate(double[] xvals, double[] yvals)	Computes an interpolating function for the data set.
<T extends CalculusFieldElement<T>> FieldPolynomialSplineFunction<T>	interpolate(T[] xvals, T[] yvals)	Computes an interpolating function for the data set.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AkimaSplineInterpolator

public AkimaSplineInterpolator()

Simple constructor.

This constructor is equivalent to call AkimaSplineInterpolator(false), i.e. to use original Akima weights

Since:

2.1

AkimaSplineInterpolator

public AkimaSplineInterpolator(boolean useModifiedWeights)

Simple constructor.

The weight modification is described in Makima Piecewise Cubic Interpolation. It attempts to avoid overshoots near near constant slopes sub-samples.

Parameters:

useModifiedWeights - if true, use modified weights to avoid overshoots

Since:

2.1

Method Detail

interpolate

public PolynomialSplineFunction interpolate(double[] xvals,
                                            double[] yvals)
                                     throws MathIllegalArgumentException

Computes an interpolating function for the data set.

Specified by:

interpolate in interface UnivariateInterpolator

Parameters:

xvals - the arguments for the interpolation points

yvals - the values for the interpolation points

Returns:

a function which interpolates the data set

Throws:

MathIllegalArgumentException - if xvals and yvals have different sizes.

MathIllegalArgumentException - if xvals is not sorted in strict increasing order.

MathIllegalArgumentException - if the size of xvals is smaller than 5.

interpolate

public <T extends CalculusFieldElement<T>> FieldPolynomialSplineFunction<T> interpolate(T[] xvals,
                                                                                        T[] yvals)
                                                                                 throws MathIllegalArgumentException

Computes an interpolating function for the data set.

Specified by:

interpolate in interface FieldUnivariateInterpolator

Type Parameters:

T - the type of the field elements

Parameters:

xvals - the arguments for the interpolation points

yvals - the values for the interpolation points

Returns:

a function which interpolates the data set

Throws:

MathIllegalArgumentException - if xvals and yvals have different sizes.

MathIllegalArgumentException - if xvals is not sorted in strict increasing order.

MathIllegalArgumentException - if the size of xvals is smaller than 5.

Since:

1.5